Products / Trust Flight Recorder for AI
Trust Flight Recorder for AI
Trust Flight Recorder for AI is the proof layer for AI-native systems and consequential automated actions. It preserves replayable evidence beyond runtime.
Proof-first behavior
- Proof at decision time
- Replayable evidence
- Portable verification
What it is
Proof for consequential AI actions.
It preserves the evidence behind trust decisions at the point of consequence.
The proof layer for AI-native systems. Preserve legality at decision time, seal witnesses, replay consequential actions, and export portable trust artifacts.
It captures trust-relevant state at the point of consequence, then keeps the resulting proof available for replay, review, and independent verification without requiring the original runtime to stay alive.
What it preserves
- Trust state at decision time
- Witness-grade evidence
- Replay context
- Portable proof objects
Proof flow timeline
Action to verification, in the order consequence occurs.
- 01
Action occurs
A consequential AI action begins in an assistant, agent, workflow, or autonomous control loop.
- 02
Trust state computed
Trust-relevant conditions are evaluated at the point of consequence instead of only after the fact.
- 03
Legality/proof generated
The decision is translated into evidence that can later support legal, security, or operational review.
- 04
Witness sealed
The supporting witness is preserved so the original judgment can be reconstructed without drift.
- 05
Replay supported
The consequential action can be replayed with the surrounding proof context intact.
- 06
Proof exported
Portable proof artifacts can leave the runtime and flow back into the systems you already use.
- 07
Verification beyond runtime
Independent reviewers can verify the record later, even outside the original environment.
What the timeline proves
- Trust state at decision time
- Witness-grade evidence
- Replayable path back to the event
- Portable proof beyond runtime
Credibility cues
The proof layer sits beside the system. It preserves the boundary, keeps replay viable, and leaves an artifact that can be verified later without the original runtime.
Why it is different
Logs capture activity. This preserves proof.
The product exists to make consequential actions defensible, not just visible.
What it is not
- Logs
- Generic observability
- After-the-fact governance
- Model monitoring alone
What it proves
- Proves what happened
- Proves it was allowed
- Preserves replayable evidence
- Exports proof beyond runtime
Architecture / stack fit
Beside the stack, not inside it.
Bridge-first integration keeps proof bounded without forcing a new raw-data center.
Trust Flight Recorder for AI adds a proof layer around selected consequential decision points. It does not ask you to rebuild your stack, turn all telemetry into a single lake, or absorb new runtime weight just to preserve evidence.
The result is a bounded trust overlay that can sit alongside observability, workflow, AI, and control systems while still producing proof objects that flow back into the environment you already operate.
Stack fit
- Bridge-first integration alongside existing observability, workflow, AI, and control systems.
- No centralized raw-data gravity. Proof is captured selectively at consequential decision points.
- No runtime bottleneck. The page sits beside the system instead of becoming the system.
- Overlay, not replacement. Proof objects can flow back into the tools you already use.
Use cases
Built for consequential workflows.
AI-native systems
Prove and replay consequential actions taken by copilots, agents, or automated workflows.
Security and incident response
Preserve evidence that can support investigation, defensibility, and post-incident reconstruction.
Regulated environments
Carry trust artifacts into audits, oversight, and review without forcing crude centralization.
Sovereign and cross-boundary systems
Preserve portable proof that remains meaningful outside the original runtime.
Autonomous and industrial systems
Capture trust state at the point of action in environments where consequence is real and hard to reverse.
CTA
Make the decision provable.
We can show how Trust Flight Recorder for AI fits your current stack, where the proof boundary should live, and what a pilot path looks like in practice.
What happens next
- See the walkthrough
- Scope a pilot conversation
- Map the integration boundary